Secondary Logo

Share this article on:

Correlation Between Cardiac Autonomic Modulation in Response to Orthostatic Stress and Indicators of Quality of Life, Physical Capacity, and Physical Activity in Healthy Individuals

Gonçalves, Thiago R.1; Farinatti, Paulo de Tarso Veras2; Gurgel, Jonas L.3; da Silva Soares, Pedro P.1

Journal of Strength and Conditioning Research: May 2015 - Volume 29 - Issue 5 - p 1415–1421
doi: 10.1519/JSC.0000000000000769
Original Research

Gonçalves, TR, Farinatti, PTV, Gurgel, JL, and da Silva Soares, PP. Correlation between cardiac autonomic modulation in response to orthostatic stress and indicators of quality of life, physical capacity, and physical activity in healthy individuals. J Strength Cond Res 29(5): 1415–1421, 2015—Increased heart rate variability (HRV) at rest is frequently associated to maximal oxygen uptake (

), physical activity, and markers of quality of life (QoL). However, the HRV has not been observed during physical exercise or orthostatic (ORT) challenge. This study investigated the associations of HRV changes (ΔHRV) from rest at supine (SUP) to ORT positions with

, physical activity level, and QoL in young adults. Cardiac autonomic modulation was assessed by spectral analysis of R-R time series measured from SUP to ORT positions in 15 healthy volunteers (26 ± 7 years). Questionnaires were applied for evaluation of QoL (SF-36 score), to estimate

, and to quantify physical activity (Baecke Sport Score). All HRV indices at SUP, but not ORT, strongly correlated to QoL, estimated

, and physical activity. The ΔHRV from SUP to ORT showed significant correlations with all questionnaire scores (r = 0.52–0.61 for low frequency and r = −0.61 to −0.65 for high frequency, p ≤ 0.05). Higher vagal activity at rest and greater changes in adrenergic and parasympathetic modulation from SUP to ORT were detected in the volunteers exhibiting higher scores of QoL, estimated

, and physical activity. Taken together, the level of neural adaptations from resting SUP position to active standing, and physical activity and QoL questionnaires seem to be a simple approach to understand the physiological and lifestyle adaptations to exercise that may be applied to a large sample of subjects in almost any sports facilities at a low cost.

1Laboratory of Experimental and Applied Exercise Physiology, Department of Physiology and Pharmacology, Biomedical Institute, Fluminense Federal University, Niterói, Brazil;

2Physical Activity and Health Promotion Laboratory, Physical Education Institute, Rio de Janeiro State University, Rio de Janeiro, Brazil; and

3Research Group on Biomechanics, Physical Education Institute, Fluminense Federal University, Niterói, Brazil

Address correspondence to Pedro P. da Silva Soares,

Back to Top | Article Outline


The autonomic regulation of the heart rate (HR) varies continually because of activation or inhibition of sympathetic and parasympathetic branches of the autonomic nervous system (ANS) (2). The contribution of ANS to spontaneous fluctuation of HR can be assessed by spectral analysis of HR time series, known as heart rate variability (HRV) (22). This technique provides a noninvasive and reliable assessment of the cardiac autonomic function (1,27).

The autonomic markers obtained by spectral analysis are often calculated from recordings in resting supine (SUP) or sitting (SIT) positions, assuming that HRV at these body positions would represent individuals' parasympathetic predominance and presenting higher values for HRV indices. In fact, autonomic markers obtained at these conditions are used as reference values, reflecting individual's neural control of HR (1,20,25).

The literature shows that HRV at rest decreases throughout aging (9) and the vagal activity is related to greater levels of physical capacity, physical activity, and quality of life (QoL) (5,15). These correlations among physical capacity, physical activity, QoL (health indicators), and HRV have been obtained when HRV is measured in SUP (5). Autonomic markers are also obtained in SIT position, and these values have been reported as either similar (7) or lower (6) than HRV measured at SUP.

There is also some evidence that during orthostatic (ORT) position, significant correlations are found between HRV indices and physical capacity (13). However, little is known about the association among HRV assessed in the ORT position and markers of QoL, physical capacity, and physical activity. A simple article investigated the autonomic change from SUP to ORT and showed an association with individuals who performed higher volumes of exercise (12).

The adjustments to autonomic maneuvers (ΔHRV) and their possible relationship with health indicators are still debated (17) and thereby may reveal additional information not found when only absolute HRV values are informed. Changing in body position from SUP or SIT to ORT shifts the autonomic balance toward sympathetic dominance, inducing adjustments that may better describe the autonomic modulation to the heart in comparison with measures taken at rest occurring association with physical capacity, physical activity, and QoL.

It is important for the practical application to verify the autonomic adjustment provoked by a simple change in body position and if this autonomic variation is associated with indicators of health, such as QoL, physical capacity and physical activity. A higher cardiac autonomic adjustment may be an indicator of better physical performance, allowing a better understanding of the associations between cardiovascular parameters, physical conditioning, and lifestyle.

Hence, the aim of the present study was to correlate ΔHRV when changing the body position from SUP to SIT and then to ORT with markers of QoL, habitual physical activity, and physical capacity in healthy subjects. Individuals with higher indicators of QoL, physical activity, and physical capacity may present greater cardiac autonomic modulation, both vagal and adrenergic, by the differences of the HRV indexes between ORT and SUP positions.

Back to Top | Article Outline


Experimental Approach to the Problem

The present study evaluates cardiac autonomic modulation changing in 3 different body positions: SUP, SIT, and ORT. Questionnaires for QoL, physical capacity, and physical activity were then applied. Heart rate variability indices, both time and frequency domains, were directly compared among each body position. Absolute HRV values and ΔHRV markers from SUP to SIT, SIT to ORT, and SUP to ORT were correlated with the QoL, physical capacity, and physical activity indicators.

Participants stayed at rest for 10 minutes in SUP position to allow adaptation to the laboratory environment and to adjust the equipments. Heart rate was recorded for 20 minutes in each position (SUP, SIT, and ORT). After posture change maneuvers, the volunteers answered questionnaires to assess QoL, habitual physical activity, and physical capacity.

A seat of manual tilt (Dentemed, Belo Horizonte, Brazil) was used to assess HRV in SUP and SIT positions. The change of SUP to SIT and then to ORT was performed manually. The HRV was analyzed only in the last 10 minutes of each position to avoid bias related to the change of body position. Heart rate was recorded beat-by-beat using an R-R monitor (RS810; Polar Electro Oy, Oulu, Finland) (10). The entire experiment was executed in the afternoon between 14:00 and 16:00 PM, in a silent room with the temperature between 22 and 24° C, from April to August of the calendar year.

Back to Top | Article Outline


In this study, 15 volunteers (11 men), who were not physically active (non-practicing regular physical exercise) and were healthy (26 ± 7 years, 72.2 ± 10.0 kg, 172.1 ± 8.3 cm, and 24.3 ± 2.6 kg·m−2 of body mass index), underwent a 1-day experimental protocol. The participants were not using medications that altered the ANS or dietary supplements. All volunteers were instructed to avoid strenuous physical activity (≥5 metabolic equivalent [METs]) in the day previous to the testing, to have a good night's sleep, to have a regular meal 3 hours preceding the test, and not to drink beverages containing caffeine on the day of the test. The procedures were approved by institutional local ethics committee on research involving human individuals and in accordance with the Declaration of Helsinki, and all volunteers provided informed consent before participating in the study.

Back to Top | Article Outline


Time series R-R intervals were analyzed by an automatic algorithm for artifacts removal and the beat-by-beat R-R interval series were then converted into equally spaced time series with 200-millisecond intervals using cubic spline interpolation. Spectral analysis was processed using Fast Fourier Transform with the Welch's method, a Hanning window with 50% overlap using a customized algorithm (24) (Matlab 6.0; Mathworks Inc., Natick, MA, USA).

Time domain analysis consisted in measures of R-R intervals (average of all normal R-R intervals), SDNN (standard deviation of all normal R-R intervals), NN50 (normal R-R intervals differing more than 50 milliseconds of its opposite), pNN50 (percentage of normal R-R intervals differing more than 50 milliseconds of its adjacent), and RMSSD (square root of the sum of successive differences between adjacent normal R-R intervals squared). In the frequency domain, the power spectrum density function was integrated in the 3 classical frequency bands as follows: (a) very low frequency band (VLF: 0.01–0.04 Hz), (b) low frequency band (LF: 0.04–0.15 Hz), (c) high frequency band (HF: 0.15–0.40 Hz) (1,27).

The HF was used as an index of vagal modulation, whereas LF was considered primarily as a parameter of sympathetic nervous system influence (8,18,21). The spectral values were expressed as normalized units (nu), which were calculated by dividing the power of each component by total power (TP) from which VLF component had been subtracted, and then multiplying this value by 100 (LFnu and HFnu for normalized LF and HF powers, respectively) (1,21). The LF/HF ratio was adopted as a marker of sympathovagal balance (23).

The QoL, habitual physical activity, and physical capacity were assessed by means of specific questionnaires. The QoL was quantified using the SF-36 questionnaire (11), with scores varying from 0 to 100. The domains of the SF-36 are grouped to define 3 categories: functional status (4 domains), well-being (3 domains), and overall health (1 domain). The overall health (SF-36 score) was adopted as QoL marker.

The habitual physical activity and physical capacity were assessed by Baecke et al. (3) and Wísen et al. (30) questionnaires, respectively. The Baecke questionnaire is a widely used instrument (26) that provides a score related to the energy expenditure in physical activities within the past 12 months. It provides indexes for activities performed at home, work, and free time, including sports and leisure. In this study, only the “sports index” was considered because all the volunteers presented similar values for the home and work activities. The Wisen questionnaire was used to estimate the maximal oxygen uptake (estimated

). This questionnaire is a non-exercise instrument frequently used to estimate the cardiorespiratory capacity in clinical and epidemiological sets (30).

Back to Top | Article Outline

Statistical Analyses

Normality of data was ratified by univariate analysis. All data were expressed as mean (SD). The HRV in SUP, SIT, and ORT positions was compared by means of analysis of variance for repeated measures followed by Tukey post hoc test in case of significant F ratios. Natural logarithm was applied to adjust variables in the frequency domain of TP, VLF, LF, and HF. The Pearson's correlation test was applied to verify the association between HRV and ΔHRV with QoL, physical activity, and physical capacity markers. In all cases, the significance level was set at α ≤ 0.05, and the calculations were made by the GraphPad Prism 5.0 software (GraphPad Software Inc, San Diego, CA, USA). The sample size of 15 subjects provides a medium statistical power for the study (beta = 0.86).

Back to Top | Article Outline


Participant's respiratory rate (breaths·min−1) was evaluated visually and did not achieve significant differences among the 3 body positions (SUP = 17.8 ± 3.4, SIT = 18.1 ± 4.1, and ORT = 19.1 ± 2.7, p > 0.05). Table 1 presents data obtained for the HRV time and frequency domain. Only R-R and RMSSD exhibited significant differences between SUP and SIT (SUP > SIT, p < 0.001). However, the HRV assessed in ORT was significantly lower compared with SIT and SUP in almost all time domain markers (p ≤ 0.05). Comparisons in the frequency domain showed significant differences between SUP and SIT for lnHF, LFnu, and HFnu (p ≤ 0.05). Differences between SIT and ORT were detected for lnHF, LFnu, HFnu, and LF/HF (p ≤ 0.05). Finally, only lnVLF and lnLF were not significantly different when comparing the markers obtained in SUP and ORT conditions.

Table 1

Table 1

Figure 1 exhibits the patterns of LFnu and HFnu in SUP, SIT, and ORT positions. A decrease of 20% was observed in HFnu and 17% increase in sympathetic index in LFnu within the change from SUP to SIT, suggesting vagal withdrawal and an increase in the sympathetic drive, respectively. The change from SUP to ORT induced a decrease of 69% in the vagal index (p < 0.001) and an increase of 55% in the sympathetic index (p < 0.001). Figure 1 also presents ΔLFnu and ΔHFnu within the changes from SUP-SIT, SIT-ORT, and SUP-ORT showing an increased of the magnitude of the cardiac autonomic modulation for both the branches of ANS.

Figure 1

Figure 1

The results in mean (SD) for the questionnaires were as follows: SF-36 score = 69.3 (12.9), Baecke Sport Score = 3.33 (0.79), and estimated

= 40.2 (5.77) mL·kg−1·min−1. Table 2 presents the correlations between HRV and ΔHRV with the SF-36 score, Baecke Sport Score, and estimated

of the subjects.

Table 2

Table 2

The best associations were obtained for the values assessed in SUP position and ΔHRV from SUP-ORT. The dispersion of data among ΔHRV from ORT-SUP and questionnaires indicators are presented in Figure 2.

Figure 2

Figure 2

Back to Top | Article Outline


The present study investigated the changes in the autonomic modulation to the heart as reflected by the HRV assessed in different body positions (SUP, SIT, and ORT) and their relationships between markers of QoL, habitual physical activity, and physical capacity in young healthy adults. The results indicated that the best associations were obtained between these markers of QoL and physical capacity and ΔHRV from SUP to ORT and HRV in SUP position.

Previous studies investigating the HRV at rest associated with ORT change (14) or passive tilt test (19,29) reported similar results to the present research with regard to adrenergic and vagal influences on HR dynamics, both in time and frequency domains. Using the nonlinear method of detrended fluctuation analysis, Castiglioni et al. (6) observed significant differences in HRV indexes from SUP to SIT. Furthermore, these authors reported differences in HRV indexes between younger and older individuals, when changing from SUP to SIT positions and during exercise.

There is a difference between evaluating the HRV at rest in SUP, SIT, and ORT positions because of an increased sympathetic drive. These differences may have influenced the magnitude of association between the autonomic indexes and the markers of QoL, physical activity, and physical capacity. In the present study, the HRV in SIT position was associated significantly with estimated

and Baecke Sport Score, but the coefficients were lower compared with those obtained in SUP position. The HRV in ORT position showed significant correlation only to Baecke Sport Score and none to estimated

and QoL. However, the best associations were not found for HRV measured at rest but for the ΔHRV from SUP to ORT. Hence, the body position has influenced the degree of association between HRV at rest and the presently observed markers. Moreover, the ΔHRV may better describe the associations with all markers than assessment at rest only, especially in comparison with SIT and ORT. Evidently, these results should be taken into account when designing experimental protocols in future research.

Some previous research evaluated the association between QoL and physical activity markers with HRV indexes (5,28). Buchheit et al. (5) demonstrated that physically active adults aged 61 ± 4 years had higher scores in SF-36 and Baecke questionnaires, as well as higher vagal activity reflected by the HF/(LF + HF) ratio compared with less active counterparts. Another study by the same group showed a significant association between maximal aerobic fitness and HRV, and between scores from Baecke questionnaire and postexercise HR recovery (4). Sandercock et al. (28) demonstrated that individuals with moderate-to-high habitual physical activity levels had higher R-R intervals, SDNN, and RMSSD compared with those with low physical activity level. Gilder and Ramsbottom (12) measured the ΔHRV in response to ORT test in 72 young women with low or high physical activity levels. The HRV was reported to relate to the volume of regular exercise—women who performed high exercise volume showed greater ΔHRV from SUP to ORT position than those with low exercise volume, and also greater HRV at rest in SUP position.

In the present study, significant correlations between HRV and Baecke Sport Score, particularly between ΔHRV and Baecke Sport Score, were found. It can be therefore speculated that individuals with higher sport index have greater HRV and greater magnitude of vagal modulation because of postural stress. Hence, there would be a relationship between habitual physical activity and autonomic modulation, contrarily to what has been postulated by previous research (28), but ratifying the results of others (5). The present results indicate that postural stress when changing from SUP to ORT may be a useful marker for the association between autonomic modulation and indicators of health.

The correlations presently observed between ΔHRV and scores from the different questionnaires also suggest that there would be an increased adrenergic and vagal modulation in volunteers with greater values of QoL, physical activity, and physical capacity, which is quite compatible to the available evidence. Subjects exhibiting higher estimated

, SF-36 score, and Baecke Sport Score seem to have better adjustment of vagal and adrenergic cardiac modulation when changing from SUP to standing upright.

Laitinen et al. (16) investigated the ΔHRV within changing position from SUP to ORT in different age groups—group 1 (32–39 years), group 2 (40–59 years), and group 3 (60–77 years). Only group 1 showed significant difference in the sympathovagal balance and vagal activity from SUP to ORT. It is, therefore, possible that in older individuals the adjustment mechanisms of adrenergic and vagal modulation under position stress are compromised (16). Lucini et al. (17) have also observed reduced ΔHRV in elder subjects compared with younger subjects.

There are some limitations in the present study that must be highlighted. Body positions' orders were not randomized, and the change from SUP to SIT position was active. Although there might be some influence of muscle contractions and effort to assume SIT position, the HRV analysis was performed after 10 minutes in each body position within a 20-minute period, enough time for adaptations and when the signals were stable. Most researchers investigate the autonomic modifications to the ORT challenge from SUP to active or passive standing, so the current approach is in agreement with others and may be comparable in literature.

It is known that individuals with better physical fitness or who are young have higher HRV and increased vagal modulation at rest, serving as a tool of practice application for prescribing physical exercise and prognosis of health (4). The present study quantified the sympathetic-parasympathetic balance difference between the SUP and SIT positions, making possible to compare HRV indices obtained in one body position extrapolating to the other. It means that in SIT position, the LF component power, in normalized units, is 17% higher than during SUP, and the HF component power, also in normalized units, is around 20% lower as compared with SUP. Additionally, the use of changes rather than absolute values, obtained by a simple task as the active standing maneuver, may provide valuable information regarding the cardiac autonomic modulation and indicators of health obtained by questionnaires that are readily applied by coaches, physicians, and trainers.

In summary, the present study showed an increase in sympathetic and a decrease in vagal modulation along continuous recording of HR within changes in body position, with the greatest magnitude from SUP to active standing. There was a higher significant correlation in HRV indices at rest in SUP position and physical capacity, physical activity, and QoL than SIT position. There is a considerable difference for autonomic markers between SUP and SIT positions that must take into consideration for comparisons. The best associations were those observed between these markers and ΔHRV from SUP to ORT position and, also, in SUP position, which should be considered in further research protocols and offer additional information. These results suggest that subjects exhibiting higher QoL, habitual physical activity, and physical capacity also have higher vagal modulation of the heart at rest in SUP position and increased adrenergic and parasympathetic adjustments when changing from SUP to standing.

Back to Top | Article Outline

Practical Applications

Active standing is a simple and practical maneuver to challenge the cardiovascular system neural control without the need of exercise. The present data showed robust associations between HRV, a marker of cardiac autonomic regulation, and physical activity indicators obtained from a physical activity questionnaire.

The level of neural adaptations from resting SUP position to active standing seems to add relevant information regarding the HR control. Taken together, active standing, physical activity, and QoL questionnaires seem to be a simple approach to understand the physiological and lifestyle adaptations to exercise that may be applied to a large sample of subjects in almost any sports facilities at a low cost.

Back to Top | Article Outline


The present work received partial financial support from Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq, Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro—FAPERJ, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES.

Back to Top | Article Outline


1. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 93: 1043–1065, 1996.
2. Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science 213: 220–222, 1981.
3. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 36: 936–942, 1982.
4. Buchheit M, Gindre C. Cardiac parasympathetic regulation: Respective associations with cardiorespiratory fitness and training load. Am J Physiol Heart Circ Physiol 291: H451–H458, 2006.
5. Buchheit M, Simon C, Charloux A, Doutreleau S, Piquard F, Brandenberger G. Heart rate variability and intensity of habitual physical activity in middle-aged persons. Med Sci Sports Exerc 37: 1530–1534, 2005.
6. Castiglioni P, Parati G, Civijian A, Quintin L, Di Rienzo M. Local scale exponents of blood pressure and heart rate variability by detrended fluctuation analysis: Effects of posture, exercise, and aging. IEEE Trans Biomed Eng 56: 675–684, 2009.
7. Chan HL, Lin MA, Chao PK, Lin CH. Correlates of the shift in heart rate variability with postures and walking by time-frequency analysis. Comput Methods Programs Biomed 86: 124–130, 2007.
8. Cooley RL, Montano N, Cogliati C, van de Borne P, Richenbacher W, Oren R, Somers VK. Evidence for a central origin of the low-frequency oscillation in RR-interval variability. Circulation 98: 556–561, 1998.
9. Folkow B, Svanborg A. Physiology of cardiovascular aging. Physiol Rev 73: 725–764, 1993.
10. Gamelin FX, Berthoin S, Bosquet L. Validity of the polar S810 heart rate monitor to measure R-R intervals at rest. Med Sci Sports Exerc 38: 887–893, 2006.
11. Garratt AM, Ruta DA, Abdalla MI, Buckingham JK, Russell IT. The SF36 health survey questionnaire: An outcome measure suitable for routine use within the NHS? BMJ 306: 1440–1444, 1993.
12. Gilder M, Ramsbottom R. Change in heart rate variability following orthostasis relates to volume of exercise in healthy women. Auton Neurosci 143: 73–76, 2008.
13. Grant CC, Clark JR, van Rensburg DC, Viljoen M. Relationship between exercise capacity and heart rate variability: Supine and in response to an orthostatic stressor. Auton Neurosci 151: 186–188, 2009.
14. Howorka K, Pumprla J, Jirkovska A, Lacigova S, Nolan J. Modified orthostatic load for spectral analysis of short-term heart rate variability improves the sensitivity of autonomic dysfunction assessment. J Diabetes Complications 24: 48–54, 2010.
15. Iellamo F, Legramante JM, Pigozzi F, Spataro A, Norbiato G, Lucini D, Pagani M. Conversion from vagal to sympathetic predominance with strenuous training in high-performance world class athletes. Circulation 105: 2719–2724, 2002.
16. Laitinen T, Niskanen L, Geelen G, Lansimies E, Hartikainen J. Age dependency of cardiovascular autonomic responses to head-up tilt in healthy subjects. J Appl Physiol (1985) 96: 2333–2340, 2004.
17. Lucini D, Cerchiello M, Pagani M. Selective reductions of cardiac autonomic responses to light bicycle exercise with aging in healthy humans. Auton Neurosci 110: 55–63, 2004.
18. Malliani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation 84: 482–492, 1991.
19. Martinelli FS, Chacon-Mikahil MP, Martins LE, Lima-Filho EC, Golfetti R, Paschoal MA, Gallo-Junior L. Heart rate variability in athletes and nonathletes at rest and during head-up tilt. Braz J Med Biol Res 38: 639–647, 2005.
20. Martinmaki K, Rusko H, Kooistra L, Kettunen J, Saalasti S. Intraindividual validation of heart rate variability indexes to measure vagal effects on hearts. Am J Physiol Heart Circ Physiol 290: H640–H647, 2006.
21. Montano N, Cogliati C, Porta A, Pagani M, Malliani A, Narkiewicz K, Abboud FM, Birkett C, Somers VK. Central vagotonic effects of atropine modulate spectral oscillations of sympathetic nerve activity. Circulation 98: 1394–1399, 1998.
22. Montano N, Porta A, Cogliati C, Costantino G, Tobaldini E, Casali KR, Iellamo F. Heart rate variability explored in the frequency domain: A tool to investigate the link between heart and behavior. Neurosci Biobehav Rev 33: 71–80, 2009.
23. Montano N, Ruscone TG, Porta A, Lombardi F, Pagani M, Malliani A. Power spectrum analysis of heart rate variability to assess the changes in sympathovagal balance during graded orthostatic tilt. Circulation 90: 1826–1831, 1994.
24. Montenegro RA, Farinatti Pde T, Fontes EB, Soares PP, Cunha FA, Gurgel JL, Porto F, Cyrino ES, Okano AH. Transcranial direct current stimulation influences the cardiac autonomic nervous control. Neurosci Lett 497: 32–36, 2011.
25. Perini R, Veicsteinas A. Heart rate variability and autonomic activity at rest and during exercise in various physiological conditions. Eur J Appl Physiol 90: 317–325, 2003.
26. Philippaerts RM, Westerterp KR, Lefevre J. Comparison of two questionnaires with a tri-axial accelerometer to assess physical activity patterns. Int J Sports Med 22: 34–39, 2001.
27. Sandercock GR, Bromley PD, Brodie DA. The reliability of short-term measurements of heart rate variability. Int J Cardiol 103: 238–247, 2005.
28. Sandercock GR, Hardy-Shepherd D, Nunan D, Brodie D. The relationships between self-assessed habitual physical activity and non-invasive measures of cardiac autonomic modulation in young healthy volunteers. J Sports Sci 26: 1171–1177, 2008.
29. Tulppo MP, Hughson RL, Makikallio TH, Airaksinen KE, Seppanen T, Huikuri HV. Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics. Am J Physiol Heart Circ Physiol 280: H1081–H1087, 2001.
30. Wisen AG, Farazdaghi RG, Wohlfart B. A novel rating scale to predict maximal exercise capacity. Eur J Appl Physiol 87: 350–357, 2002.

active standing; heart rate variability; health; physical fitness; body position

Copyright © 2015 by the National Strength & Conditioning Association.